
for convolutions, and grouped convolutions with \(g\)  groups, where the \(n\) -th group considers \(G_{n}^{(l-1)} = C^{(l-1)}/g\)  channels, respectively. Additionally, it must hold that \(\mod {(C^{(l)}, g)}=0\) . In particular, \(X_i^{(l-1)}\)  and \(X_j^{(l)}\) , denote the \(i\) -th input channel and the \(j\) -th output channel of the \(l\) -th layer, respectively. \(K_{ij}^{(l)}\)  is the filter that maps \(X_i^{(l-1)}\)  to \(X_j^{(l)}\)  and \(t_j^{(l)}\)  is the bias term associated with the \(j\) -th output of layer \(l\) .
Therefore, it can be deduced that with grouped convolution, the complexity of the convolutional layer is reduced by a factor equal to \(g\) .
Regarding the number of parameters to be offloaded to the MT, we observe from [1]} that the fully connected (FC) layer constitutes the largest overhead with \(1280 \times 256 + 256 = 327,936\)  single precision floating point numbers, which correspond to \(327,936 \times 32\)  bits. In order to reduce the amount of bits that have to be offloaded to the UE, we propose two approaches: i) replace the weights of the FC layer in the encoder with binary FC weights [2]}, ii) prune the least significant parameters of the encoder network.
